


import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		IFPipeline
			_UpperCamelCase		:       List[str] 			=		TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
			_UpperCamelCase		:       List[str] 			=		TEXT_TO_IMAGE_BATCH_PARAMS
			_UpperCamelCase		:       Tuple 			=		PipelineTesterMixin.required_optional_params - {'latents'}




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self._get_dummy_components()




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


						if str(snake_case  ).startswith("mps"  ):
									UpperCamelCase__       =							torch.manual_seed(snake_case  )
						else:
									UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )

						UpperCamelCase__       =							{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}

						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != "cuda"   ,    reason="float16 requires CUDA"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().test_save_load_floataa(expected_max_diff=1E-1  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_save_load_local()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2   ,    )







			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,    reason="XFormers attention is only available with CUDA and `xformers` installed"   ,    )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )







@slow
@require_torch_gpu
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,    variant="fp16"   ,    torch_dtype=torch.floataa  )

						UpperCamelCase__       =							IFSuperResolutionPipeline.from_pretrained(
						    "DeepFloyd/IF-II-L-v1.0"   ,    variant="fp16"   ,    torch_dtype=torch.floataa   ,    text_encoder=snake_case   ,    tokenizer=snake_case  )

						# pre compute text embeddings and remove T5 to save memory

						pipe_a.text_encoder.to("cuda"  )

						UpperCamelCase__,   UpperCamelCase__       =							pipe_a.encode_prompt("anime turtle"   ,    device="cuda"  )

						del pipe_a.tokenizer
						del pipe_a.text_encoder
						gc.collect()

						UpperCamelCase__       =							None
						UpperCamelCase__       =							None

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# img2img

						UpperCamelCase__       =							IFImgaImgPipeline(**pipe_a.components  )
						UpperCamelCase__       =							IFImgaImgSuperResolutionPipeline(**pipe_a.components  )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if_imgaimg(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# inpainting

						UpperCamelCase__       =							IFInpaintingPipeline(**pipe_a.components  )
						UpperCamelCase__       =							IFInpaintingSuperResolutionPipeline(**pipe_a.components  )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if_inpainting(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						_start_torch_memory_measurement()

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )
						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    num_inference_steps=2   ,    generator=snake_case   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (64, 64, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 13 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )

						# pipeline 2

						_start_torch_memory_measurement()

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )

						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(0  )  ).to(snake_case  )

						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    image=snake_case   ,    generator=snake_case   ,    num_inference_steps=2   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (256, 256, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						_start_torch_memory_measurement()

						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(0  )  ).to(snake_case  )

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )

						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    image=snake_case   ,    num_inference_steps=2   ,    generator=snake_case   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (64, 64, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )

						# pipeline 2

						_start_torch_memory_measurement()

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )

						UpperCamelCase__       =							floats_tensor((1, 3, 256, 256)   ,    rng=random.Random(0  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(0  )  ).to(snake_case  )

						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    image=snake_case   ,    original_image=snake_case   ,    generator=snake_case   ,    num_inference_steps=2   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (256, 256, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						_start_torch_memory_measurement()

						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(0  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(1  )  ).to(snake_case  )

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )
						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    image=snake_case   ,    mask_image=snake_case   ,    num_inference_steps=2   ,    generator=snake_case   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (64, 64, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )

						# pipeline 2

						_start_torch_memory_measurement()

						UpperCamelCase__       =							torch.Generator(device="cpu"  ).manual_seed(0  )

						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(0  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 256, 256)   ,    rng=random.Random(0  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 256, 256)   ,    rng=random.Random(1  )  ).to(snake_case  )

						UpperCamelCase__       =							pipe_a(
						    prompt_embeds=snake_case   ,    negative_prompt_embeds=snake_case   ,    image=snake_case   ,    mask_image=snake_case   ,    original_image=snake_case   ,    generator=snake_case   ,    num_inference_steps=2   ,    output_type="np"   ,    )

						UpperCamelCase__       =							output.images[0]

						assert image.shape == (256, 256, 3)

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"  )
						assert_mean_pixel_difference(snake_case   ,    snake_case  )



def 							UpperCamelCase_(					)->		Union[str, Any]:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()






import requests
from bsa import BeautifulSoup
def 							UpperCamelCase_(					_A :str = "AAPL"	)->		str:
			UpperCamelCase__       =							F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
			UpperCamelCase__       =							BeautifulSoup(requests.get(_A	).text					,  "html.parser"	)
			UpperCamelCase__       =							"My(6px) Pos(r) smartphone_Mt(6px)"
			return soup.find("div"					,  class_=class_	).find("span"	).text


if __name__ == "__main__":
						for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
												print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')


